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Dynamics of Separation Characteristics of Sieving and Flow Classification Processes

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Dynamic Flowsheet Simulation of Solids Processes

Abstract

In spite of the broad range of applications of flow and sieve classification, the physical phenomena for higher particle loadings are not completely understood. As a starting point, common models such as the one of Molerus may be used and optimized to include particle-particle and particle-wall collisions. In this contribution, it is investigated to which extent single particle models may be employed to describe the performance of a deflector wheel classifier and a circular vibratory screening machine at higher loadings. For the sieving process, the Molerus model was modified with a selectivity parameter, while for the deflector wheel, a differentiation of particles with low and high Stokes numbers was made. For high Stokes numbers, in a first approximation, the particularities of the airflow can be neglected, but the impaction behavior on the wheel blades needs to be taken into account. With the detailed knowledge of the mean airflow, a much better prediction of the separation curve can be obtained. In particular, the dynamic aspects of flow and sieving classification have been studied.

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Abbreviations

T(x):

Separation efficiency (–)

x:

Particle size (m)

x′:

Dimensionless particle size (–)

k:

Coefficient for the sharpness of cut (–)

COR:

Coefficient of restitution (–)

v:

Velocity (m s−1)

η:

Viscosity (Pa s)

ρ:

Density (kg m−3)

H:

Particle layer height (cm)

τ:

Characteristic particle relaxation time (s)

τ*:

Particle cloud relaxation time (s)

\({\dot{\text{V}}}\) :

Carrier gas volume flow rate (m3 s−1)

R:

Wheel radius (m)

h:

Height of the openings in the deflector wheel (m)

U:

Circumference (m)

Ψ2D:

2D Sphericity (–)

SEM:

Scanning electron microscope

σ:

Standard deviation (depends on related variable)

E:

Kinetic energy (J)

JKR:

Johnson-Kendall-Roberts

p:

Pressure (Pa)

E*:

Average Young’s modulus (Pa)

υ:

Poisson ratio (–)

Θ:

Angle related to the deflector wheel blade (°)

L:

Impaction length (m)

f:

Revolution rate (s−1), fine material fraction (–)

cD:

Drag coefficient (–)

Re:

Particle Reynolds number (–)

κ:

Sharpness of cut (–)

F:

Force (N)

c:

Coarse material fraction (–)

Q:

Distribution sum function (–)

a:

“Dead flow” parameter (–)

α, β:

Measure of selectivity (–)

Γ:

Dimensionless acceleration number (–)

A:

Amplitude (m)

ω:

Angular velocity (° s−1)

g:

Gravitational constant (m s−2)

q:

Density distribution (m−1)

\({\dot{\text{m}}}\) :

Massflow rate (kg s−1)

KV:

Throwing coefficient (–)

p:

Particle

air:

Air

eff:

Effective

f:

Fines

G:

Coarse

A:

Feed

25:

Particle size related to T(x) = 0.25

50:

Mean particle size

75:

Particle size related to T(x) = 0.75

3:

Mass-weighted

t:

Cut particle size related to T(x) = 0.50

v:

Volume equivalent

area-equivalent:

Projection area equivalent

perimeter-equivalent:

Projection perimeter equivalent

r:

Rebound

i:

Approach

0:

Initial

imp:

Absolute normal impaction

rad:

Radial

tan:

Tangential

rel:

Relative

w:

Wall

kin.:

Kinetic

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Author information

Authors and Affiliations

  1. Institute of Particle Technology, Technical University of Clausthal, Clausthal-Zellerfeld, Germany

    Martin Weers, Annett Wollmann & Alfred P. Weber

  2. Institute of Particle Technology and Raw Material Innovation, TH Nürnberg, Nuremberg, Germany

    Ulrich Teipel

Authors
  1. Martin Weers
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  2. Annett Wollmann
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  3. Ulrich Teipel
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  4. Alfred P. Weber
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Correspondence to Martin Weers .

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Editors and Affiliations

  1. Institute of Solids Process Engineering and Particle Technology, Hamburg University of Technology, Hamburg, Germany

    Stefan Heinrich

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Weers, M., Wollmann, A., Teipel, U., Weber, A.P. (2020). Dynamics of Separation Characteristics of Sieving and Flow Classification Processes. In: Heinrich, S. (eds) Dynamic Flowsheet Simulation of Solids Processes. Springer, Cham. https://doi.org/10.1007/978-3-030-45168-4_10

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